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Surface anomaly detection plays an important quality control role in many manufacturing industries to reduce scrap production.
W. H. Chai, S.-S. Ho, and C.-K. Goh, “Exploiting sparsity for image-based object surface anomaly detection,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Shanghai, China, Mar. 2016, pp. 1986–1990
1990
Earlier work this paper cites.
T. Jayalakshmi and A. Santhakumaran, “Statistical Normalization and Back Propagationfor Classification,” Int. J. Comput. Theory Eng. , vol. 3, no. 1, pp. 89–93, 2011
2011
Earlier work this paper cites.
2011
Earlier work this paper cites.
E. Hoffer and N. Ailon, “Deep Metric Learning Using Triplet Network,” in International Workshop on Similarity-Based Pattern Recognition , Springer, 2015, pp. 84–92
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” ArXiv14091556 Cs, Apr. 2015
2015
Earlier work this paper cites.
O. Russakovsky et al., ”ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211-252, 2015
2015
Earlier work this paper cites.
F. Chollet et al., ”Keras,” 2015. [Online]. Available: https://github.com/fchollet/keras
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Las Vegas, NV, USA, Jun. 2016, pp. 770–778
2016
Earlier work this paper cites.
D. Weimer, B. Scholz-Reiter and M. Shpitalni, ”Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection,” CIRP Annals , vol. 65, no. 1, pp. 417-420, 2016
2016
Cited alongside, same era.
M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16) , Savannah, GA, USA, Nov. 2016, pp. 265-283
2016
Cited alongside, same era.
I. Russo, I. Confente, D. M. Gligor, and N. Cobelli, “The combined effect of product returns experience and switching costs on B2B customer re-purchase intent,” J. Bus. Ind. Mark. , vol. 32, no. 5, pp. 664–676, Jun. 2017
2017
Cited alongside, same era.
Progress DataRPM, ”Anomaly Detection & Prediction Decoded: 6 Industries, Copious Challenges, Extraordinary Impact,” 2017. [Online]. Available: https://www.progress.com/docs/default-source/datarpm/progre ss_datarpm_cadp_ebook_anomaly_detection_in_6_industries.pdf?sfvrsn=82a183de_2
P. Napoletano, F. Piccoli, and R. Schettini, “Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity,” Sensors , vol. 18, no. 2, pp. 209, Jan. 2018
2018
Later among the works it cites.
R. Arora, A. Basu, P. Mianjy, and A. Mukherjee, “Understanding Deep Neural Networks with Rectified Linear Units,” in 6th International Conference on Learning Representations (ICLR) , Vancouver, BC, Canada, Apr. 2018
2018
Later among the works it cites.
L. Qiu, X. Wu, and Z. Yu, “A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection,” IEEE Access , vol. 7, pp. 15884–15893, Jan. 2019
2019
Later among the works it cites.
R. Chalapathy and S. Chawla, “Deep Learning for Anomaly Detection: A Survey,” ArXiv190103407 Cs Stat, Jan. 2019
2019
Later among the works it cites.
B. Staar, M. Lütjen, and M. Freitag, “Anomaly detection with convolutional neural networks for industrial surface inspection,” Procedia CIRP , vol. 79, pp. 484–489, 2019
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2017
Cited alongside, same era.
V. Natarajan, T.-Y. Hung, S. Vaikundam, and L.-T. Chia, “Convolutional networks for voting-based anomaly classification in metal surface inspection,” in 2017 IEEE International Conference on Industrial Technology (ICIT) , Toronto, ON, Canada, Mar. 2017, pp. 986–991
2017
Cited alongside, same era.
L. Scime and J. Beuth, ”A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process,” Additive Manufacturing , vol. 24, pp. 273-286, Dec. 2018
2018
Cited alongside, same era.
D. Racki, D. Tomazevic, and D. Skocaj, “A Compact Convolutional Neural Network for Textured Surface Anomaly Detection,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) , Lake Tahoe, NV, USA, Mar. 2018, pp. 1331–1339
2018
Cited alongside, same era.
M. Haselmann, D. P. Gruber and P. Tabatabai, ”Anomaly Detection Using Deep Learning Based Image Completion,” in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) , Orlando, FL, USA, 2018, pp. 1237-1242
2018
Cited alongside, same era.
2019
Later among the works it cites.
P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger, “MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Long Beach, CA, USA, Jun. 2019, pp. 9584–9592
2019
Later among the works it cites.
Z. Zhong, L. Zheng, G. Kang, S. Li and Y. Yang, ”Random Erasing Data Augmentation,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, pp. 13001-13008, 2020
2020
Closest in time.
S. Aburakhia, T. Tayeh, R. Myers, and A. Shami, ”A Transfer Learning Framework for Anomaly Detection Using Model of Normality,” to appear in 2020 IEEE 11th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) , Vancouver, BC, Canada, Nov. 2020
2020
Closest in time.